In general, the disclosure relates to assembling accurate, machine-intelligible, three-dimensional panoramas, and more particularly, to detecting lines and intersecting planes through two-dimensional Hough transforms.
Street-level spatial maps provide a user with a panoramic, three-dimensional map of an area, typically from the perspective of a human on the ground. Besides being of general interest, these kinds of maps can help drivers to appreciate the nuances of a driving route, or to understand what important landmarks actually look like. If the mapping data is accurate enough, three-dimensional panoramic maps can allow property tax assessors, land appraisers, and real estate professionals to do their work quickly, often eliminating the need for at least some types of site visits. Detailed spatial information can also be used to improve emergency service response times and to plan and implement disaster relief efforts, to name but a few applications.
Unfortunately, gathering detailed spatial data is a laborious process. In a typical process, a vehicle with an imaging system and a positioning or navigation system traverses roads and waterways, taking photographs and, in some cases, range measurements as it goes. Typically, the imaging system includes a camera mast mounted on a motor vehicle. The camera mast has a number of cameras on it, and may also include one or more laser systems for gathering ranging data. The navigation system typically includes a global positioning system (GPS) receiver to provide the vehicle's absolute position in space, and may also include a traditional gyroscope-based inertial navigation system, as well as encoders (i.e., sensors) on the vehicle wheels to measure relative position.
The use of a laser ranging system provides highly accurate range data. While it is possible to gather accurate range data by using stereoscopic camera systems, range data gathered by these methods can include more noise and present additional processing challenges.